Directed Trajectories Through Communication Decision Tree Using Iterative Artificial Intelligence

2. The method of claim 1, wherein the first set of node-specific learned parameters are different from the second set of node-specific learned parameters.

5. The method of claim 1, wherein the second result includes, for each worker of the one or more selected workers, a ranking of the worker or a score for the worker.

7. The method of claim 1, wherein the first machine-learning model corresponds to a particular type of machine-learning model and the second machine-learning model corresponds to a different type of machine-learning model.

8. The method of claim 1, further comprising training, at a time point and with a first training data set, the first machine-learning model to learn the first set of node-specific learned parameters, wherein the first training data set includes other unstructured data, and wherein the first set of node-specific learned parameters associate particular semantic characteristics with an experience level, skill or job characteristic for the job.

14. The computer-program product of claim 11, wherein the second result includes, for each worker of the one or more selected workers, a ranking of the worker or a score for the worker.

15. The computer-program product of claim 11, further comprising training, at a time point and with a first training data set, the first machine-learning model to learn the first set of node-specific learned parameters, wherein the first training data set includes other unstructured data, and wherein the first set of node-specific learned parameters associate particular semantic characteristics with an experience level, skill or job characteristic for the job.